Accelerating value with AI: Make your people AI-ready

AI readiness focuses on optimizing four key capabilities – People, Processes, Data, and Technology. Are you enabling your people to create and accelerate value with AI?

Artificial intelligence (AI) brings a fundamental change in how data can be harnessed for better decision-making, predictive insights, and task execution. And yet: AI is only a tool, an enabler. Its success in transforming how we do business is fully dependent on a key capability: People. Realizing the full value of AI requires having the right talent to leverage its insights and drive outcomes.

What makes your people “AI ready”? In order to be able to adopt AI as an enabling technology, they need the capabilities and capacity to deliver on your organization’s key business goals. Businesses that address the challenges, risks, and opportunities related to their talent strategy will have a competitive advantage when it comes to leveraging AI for value creation and acceleration. 

The challenges

What’s in it for them?

Every talent strategy has elements of recruit, retain, and retrain. However, in this case, there’s a talent shortage of people who understand AI and have the functional and industry knowledge to apply it, making new-hire talent hard to come by. As a result, it’s particularly important to assess your existing workforce’s AI-related understanding, abilities, and interest – and where the capability gaps are – and plan accordingly.

Gaining an AI skillset in processes and systems is a growth opportunity for your people, and a benefit to your company to minimize turnover among your more ambitious and qualified personnel. Develop a strategy to position the right people, with curiosity and a growth mindset, in the right roles to learn to effectively leverage AI, and provide the training all personnel need.

Even people who think they have no AI experience have a baseline familiarity with AI and machine learning more broadly. Dictate a text message to a friend. Instruct your smartphone to skip a song on your playlist. Tell your remote control to change the channel. AI is all around us, but we don’t always recognize it as such. Similarly, AI is embedded in many of the business technology platforms and products that your team works on daily; most of today’s cloud-based technologies incorporate some element of AI.

So in that way, we all have some level of readiness for AI. But AI isn’t as smart as it thinks it is, because machine learning requires machine teaching.

That’s where people come in.

Our interaction with AI needs to be intentional. In business, that means we need to learn how to be active practitioners in the care and handling of AI. As we interact with AI, we teach it: training the models on our specific data, patterns, and use cases. We develop it. We enable it to grow.

Preparing for change: Aligning your organization to engage on AI

Readying people for AI can’t be left up to chance. In preparation, leaders would do well to focus on three categories:

  • Skills development/training – Learning about ways to use AI, use case approaches to producing results, data and process readiness, and collaborative use of AI insights to improve business performance are all critical to AI success. Training must be tailored to ways in which your people will be applying AI, from learning how to prompt large language models (LLMs) to extracting data that provides insights to make better business decisions. AI ethics training is critical, too, including understanding the dangers of plagiarism and knowing when and how to disclose AI’s role in your processes.
  • Demand management – Leaders should be clear about their priorities and where they want to focus on deriving value from AI first. Providing a purpose and being clear on how responsibilities will change provides insight and clarity to employees. Highlight how AI is a win for the business and its people by emphasizing that AI’s value is in the outcomes derived from using its insights, not the insights alone, and aligning those outcomes with the organization’s goals.
  • Resource alignment – Once you’ve identified your demand pattern, make sure you have the resources available to support it. Are you ready to take action across your enterprise – to realign budgets, transform the back office, prioritize data quality, modernize machinery? Aspirations without a roadmap for change will likely fall short of creating the anticipated ROI from your AI investment.

Removing the rote, toward focused relevance

No matter their capacity, your people will be affected by AI. And it will impact your operations just as it does your operators. Smooth the transition by maintaining an open, ongoing dialogue with your people so they understand the benefits and impact of these changes. Emphasize how AI will ultimately empower and enable them to focus on higher-value tasks. Enable and empower early adopters to “fail forward fast”; permission to experiment helps build trust in the people you are relying upon to get this right over time.

Consider these use cases:

  • Back-office modernization derived through AI ingesting, sorting, and cleaning data. A laborious process when done manually, aggregating and analyzing data can be done by AI, freeing up people to work on the process versus in it.
  • Improved operations thanks to AI’s exacting awareness and understanding of machinery performance (and lack thereof). Your workforce can use AI insights to become more adept at recognizing the need for preventive maintenance, leading to reduced mechanical downtime, more efficient production, and greater profits.
  • Efficiency improvements executed by analyzing precise data points gathered with a goal in mind. These provide direction toward better outcomes, including in quality, reliability, and growth.
  • Scenario testing to highlight options that best serve the organization and its strategic goals. Scenario testing focuses the possibilities. Then, people – presented with these possibilities – can make informed decisions, leading to actions that will produce the most value. AI is expanding the set of scenarios people are able to evaluate, by automating scenario testing and cross-scenario scoring.

The risks

Some leaders become overly focused on the anticipated resistance of their teams to AI. However, the better approach is to ask, “How can I position my people to succeed with AI?”

There’s no single correct answer, but let these questions inform yours:

  • How can AI enable people to perform more valuable work?
  • How can AI enable people to do that work at the right time?
  • How can AI support people in uncovering insights?
  • How can we train people with experience to improve their judgment about the value, uses, and consequences of AI-developed insights?
  • When you consider AI readiness as it pertains to your workforce, it comes down to three imperatives:

Know your people.

Know your data.

Know your uses.

Armed with this knowledge, you’ll be able to make the right decisions and establish the right policies to accelerate AI value. Frame AI implementation in this way, and you’ll empower your people.

The opportunity: Call for champions

Champions see the value of AI and then adopt it. They model behaviors, ask questions, remain curious, and take meaningful action based on the insights that AI provides.

As AI continues to overtake the business landscape, leaders can seek to gain competitive advantage by scaling in one of two directions:

  1. Architect your technology landscape to simplify data stores, pathways, and AI computing resources.
  2. Foster AI champions and challenge them to identify the value in their pursuits.

We will explore in a future article the benefits of optimizing your technology landscape. (Subscribe now to make sure you don’t miss it.) But as we’ve laid out here, your people – your AI champions – are what truly moves your organization forward.

AI distills data, analyzes information, and provides possibilities. However, its benefit is in the better insights your team creates with AI and the outcomes that follow their use: Leveraging those insights into better decisions, both automated in your systems and realized by people who act upon them.

It isn’t until people effectively leverage AI and act upon the insights they generate with it that value is truly realized.


Subject matter expertise

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shawn gilronan

Shawn Gilronan

Principal, Digital Advisory Practice Leader

Dan Meers

Managing Director, Digital

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This has been prepared for information purposes and general guidance only and does not constitute legal or professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is made as to the accuracy or completeness of the information contained in this publication, and CohnReznick LLP, its partners, employees and agents accept no liability, and disclaim all responsibility, for the consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.